The persistent use of drugs in spite of costs in multiple areas of life is central to most definitions of drug dependence. A critical question is why individuals continue to choose to use drugs, rather than avoid them, despite increasing costs. Neurobiological research has begun to answer this question by investigating how drugs of abuse impact brain systems that underlie affect, motivation, and learning. Several major theories focus on sensitization in appetitive brain 'reward'systems. A growing body of evidence suggests that anhedonia and aversive learning are also critical, but relatively little work addresses how changes in human aversive learning systems are linked to drug abuse. Such work is critically needed to provide deeper and more specific links between animal and human research, and to link basic neurobiological mechanisms with human drug-taking behavior. That is the goal of this proposal. We combine a computational reinforcement learning approach, which has successfully characterized appetitive learning systems, with an experimental thermal pain model that has successfully characterized brain systems involved in generating and modulating pain and aversive expectancies. This novel combination of two successful research traditions is a powerful approach that can provide new measures of avoidance learning in healthy individuals and drug abusers and links with animal models of addiction. In Aim 1, we develop a normative model of human brain systems involved in learning to predict pain, their dynamics over time, and their relationship to avoidance behavior. Three experiments use functional magnetic resonance imaging (fMRI) to characterize brain systems involved in representing pain and avoidance learning. We focus on interactions between pain-processing and learning networks, multiple memory systems that may support complementary aspects of learning, and the effects of expectation on these processes. In Aim 2, we extend this work to characterize methamphetamine (MA) abusers, a growing but under-studied population that presents an increasing public safety and public health challenge. We will conduct a comparative study of MA abusers and controls to a) characterize differences in pain representation and avoidance learning systems;and b) use brain systems-based measures to prospectively predict patterns of drug use during a two-month follow up. Successful completion of these aims will help researchers to leverage theories and findings from animal models of addiction by linking them to human drug-use behavior. It will also provide new measures of aversive experience and aversive learning in both brain and behavior, which will provide important insights into the neurobiological causes of persistent drug abuse. Such findings can a) inform behavioral, cognitive, and pharmacological drug-abuse treatment programs;b) suggest new ways of preventing drug use from reaching the stage of clinical dependence, and c) identify sub-types of individuals that can help prevention and treatment programs be tailored more specifically to individuals. PUBLIC HEALTH RELEVANCE: Neurobiological studies of reward- and punishment-guided learning in the brain offer powerful explanations for why drug-dependent individuals continue to choose drugs in spite of substantial costs in many areas of life. Neurobiological accounts have provided theories that lead to the development of new behavioral, cognitive, and pharmacological interventions to help break the cycle of drug abuse. An important frontier in this effort is the study of how aversive brain processes and experience (physical or psychological pain) motivate continued drug use. We propose some of the first work to look at how pain-avoidance learning works at a mechanistic, computational level in the human brain, and how changes in these systems are linked to methamphetamine use patterns in abusers. Findings from these studies will inform the development of new models of drug abuse and new treatments for users, which will help to reduce the personal health costs to abusers and the public health burden.